Ozone profile retrievals from the Ozone Monitoring Instrument

Liu, X.; Bhartia, P. K.; Chance, K.; Spurr, R. J. D.; Kurosu, Thomas P.

doi:10.5194/acp-10-2521-2010

Cited by 278 publications

(408 citation statements)

References 58 publications

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“…An independent OE-based ozone profile algorithm, referred to as SOE here, was developed at the Smithsonian Astrophysical Observatory (SAO) (Liu et al, 2010a). It was shown capable of capturing tropospheric ozone signals in OMI measurements that are perturbed by convection, biomass burning, anthropogenic pollution, and transport of pollution.…”

Section: Introductionmentioning

confidence: 99%

“…In Liu et al (2010a), the profile of partial ozone columns is retrieved at 24 layers and total ozone column is just the sum of partial ozone columns at all layers. In principle, OEbased profile algorithms should have the potential to provide more accurate total ozone estimates than the two primary total ozone algorithms because of its use of a wider wavelength range (270-330 nm) than that used for total ozone (Bhartia and Wellemeyer, 2002;Veefkind et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

et al. 2015

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Abstract. The accuracy of total ozone computed from the Smithsonian Astrophysical Observatory (SAO) optimal estimation (OE) ozone profile algorithm (SOE) applied to the Ozone Monitoring Instrument (OMI) is assessed through comparisons with ground-based Brewer spectrometer measurements from 2005 to 2008. We also compare the three OMI operational ozone products, derived from the NASA Total Ozone Mapping Spectrometer (TOMS) algorithm, the KNMI (Royal Netherlands Meteorological Institute) differential optical absorption spectroscopy (DOAS) algorithm, and KNMI's Optimal Estimation (KOE) algorithm. The best agreement is observed between SAO and Brewer, with a mean difference of within 1 % at most individual stations. The KNMI OE algorithm systematically overestimates Brewer total ozone by 2 % at low and mid-latitudes and 5 % at high latitudes while the TOMS and DOAS algorithms underestimate it by ∼ 1.65 % on average. Standard deviations of ∼ 1.8 % are calculated for both SOE and TOMS, but DOAS and KOE have higher values of 2.2 % and 2.6 %, respectively. The stability of the SOE algorithm is found to have insignificant dependence on viewing geometry, cloud parameters, or total ozone column. In comparison, the KOEBrewer differences are significantly correlated with solar and viewing zenith angles and show significant deviations depending on cloud parameters and total ozone amount. The TOMS algorithm exhibits similar stability to SOE with respect to viewing geometry and total column ozone, but has stronger cloud parameter dependence. The dependence of DOAS on observational geometry and geophysical conditions is marginal compared to KOE, but is distinct compared to the SOE and TOMS algorithms. Comparisons of all four OMI products with Brewer show no apparent long-term drift, but seasonal features are evident, especially for KOE and TOMS. The substantial differences in the KOE vs. SOE algorithm performance cannot be sufficiently explained by the use of soft calibration (in SOE) and the use of different a priori error covariance matrices; however, other algorithm details cause fitting residuals larger by a factor of 2-3 for KOE.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

et al. 2015

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“…Several techniques have been used to retrieve tropospheric ozone columns, such as the tropospheric ozone residual (TOR) method using OMI total ozone and MLS stratospheric ozone column measurements (Ziemke et al, 2006;Schroeberl et al, 2007), the direct retrieval of tropospheric ozone (e.g. Liu et al, 2010;Miles et al, 2014;Tuinder et al, 2013;Safieddine et al, 2013), and limb-nadirmatching observations from SCIAMACHY (Ebojie et al, 2014). Recently, also the multi-spectral retrieval of IASI thermal infrared and GOME-2 ultra-violet measurements has been demonstrated (Cuesta et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Tropical tropospheric ozone column retrieval for GOME-2

et al. 2014

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Abstract. This paper presents the operational retrieval of tropical tropospheric ozone columns (TOCs) from the Second Global Ozone Monitoring Experiment (GOME-2) instruments using the convective-cloud-differential (CCD) method. The retrieval is based on total ozone and cloud property data provided by the GOME Data Processor (GDP) 4.7, and uses above-cloud and clear-sky ozone column measurements to derive a monthly mean TOC between 20 • N and 20 • S. Validation of the GOME-2 TOC with several tropical ozonesonde sites shows good agreement, with a high correlation between the GOME-2 and sonde measurements, and small biases within ∼ 3 DU. The TOC data have been used in combination with tropospheric NO 2 measurements from GOME-2 to analyse the effect of the 2009-2010 El Niño-Southern Oscillation (ENSO) on the tropospheric ozone distribution in the tropics. El Niño induced dry conditions in September-October 2009 resulted in relatively high tropospheric ozone columns over the southern Indian Ocean and northern Australia, while La Niña conditions in September-October 2010 resulted in a strong increase in tropospheric NO 2 in South America, and enhanced ozone in the eastern Pacific and South America. Comparisons of the GOME-2 tropospheric ozone data with simulations of the ECHAM/MESSy Atmospheric Chemistry (EMAC) model for 2009 El Niño conditions illustrate the usefulness of the GOME-2 TOC measurements in evaluating chemistry climate models (CCMs). Evaluation of CCMs with appropriate satellite observations helps to identify strengths and weaknesses of the model systems, providing a better understanding of driving mechanisms and adequate relations and feedbacks in the Earth atmosphere, and finally leading to improved models.

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“…We use a block diagonal matrix with no covariances between the tropospheric and the stratospheric blocks, although, our sensitivity studies (not shown) did not show visible effects in ozone abundances caused by this modification. In addition, we updated the retrieval's vertical pressure grid to P i = 2 −i·1.37/2 · 1000 for i = 0, 18 which follows the principles presented by Liu et al (2010) without changing the number of layers (see Table 3). We also tested how sensitive the retrieval is to the selection of the correlation length.…”

Section: Effect Of Modifications On Averaging Kernels and Errorsmentioning

confidence: 99%

“…As part of this line of instruments the Ozone Monitoring Instrument (OMI; Levelt et al, 2006a, b) on-board Earth Observing System (EOS) Aura (Schoeberl et al, 2006) satellite was launched in 2004. Currently, two algorithms are used to retrieve ozone profiles from the OMI measurements: the operational OMO3PR retrieval developed at KNMI (Kroon et al, 2011) and a scientific algorithm developed at NASA (Liu et al, 2010). Both algorithms are based on the optimal estimation retrieval technique (Rodgers, 2000) but they differ in the implementation.…”

Section: Introductionmentioning

confidence: 99%

Towards the retrieval of tropospheric ozone with the Ozone Monitoring Instrument (OMI)

et al. 2015

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Abstract.We have assessed the sensitivity of the operational Ozone Monitoring Instrument (OMI) ozone profile retrieval algorithm to a number of a priori and radiative transfer assumptions. We studied the effect of stray light correction, surface albedo assumptions and a priori ozone profiles on the retrieved ozone profile. Then, we studied how to modify the algorithm to improve the retrieval of tropospheric ozone. We found that stray light corrections have a significant effect on the retrieved ozone profile but mainly at high altitudes. Surface albedo assumptions, on the other hand, have the largest impact at the lowest layers. Choice of an ozone profile climatology which is used as a priori information has small effects on the retrievals at all altitudes. However, the usage of climatological a priori covariance matrix has a significant effect. Based on these sensitivity tests, we made several modifications to the retrieval algorithm: the a priori ozone climatology was replaced with a new tropopause-dependent climatology, the a priori covariance matrix was calculated from the climatological ozone variability values, and the surface albedo was assumed to be linearly dependent on wavelength in the 311.5-330 nm channel. As expected, we found that the a priori covariance matrix basically defines the vertical distribution of degrees of freedom for a retrieval. Moreover, our case study over Europe showed that the modified version produced over 10 % smaller ozone abundances in the troposphere which reduced the systematic overestimation of ozone in the retrieval algorithm and improved correspondence with Infrared Atmospheric Sounding Instrument (IASI) retrievals. The comparison with ozonesonde measurements over North America showed that the operational retrieval performed better in the upper troposphere/lower stratosphere (UTLS), whereas the modified version improved the retrievals in the lower troposphere and upper stratosphere. These comparisons showed that the systematic biases in the OMI ozone profile retrievals are not caused by the a priori information but by some still unidentified problem in the radiative transfer modelling. Instead, the a priori information pushes the systematically wrong ozone profiles towards the true values. The smaller weight of the a priori information in the modified retrieval leads to better visibility of tropospheric ozone structures, because it has a smaller tendency to damp the variability of the retrievals in the troposphere. In summary, the modified retrieval unmasks systematic problems in the radiative transfer/instrument model and is more sensitive to tropospheric ozone variation; that is, it is able to capture the tropospheric ozone morphology better.

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Ozone profile retrievals from the Ozone Monitoring Instrument

Cited by 278 publications

References 58 publications

Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

Validation of OMI total ozone retrievals from the SAO ozone profile algorithm and three operational algorithms with Brewer measurements

Tropical tropospheric ozone column retrieval for GOME-2

Towards the retrieval of tropospheric ozone with the Ozone Monitoring Instrument (OMI)

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